Performance Evaluation of Bitstring Representations in a Linear Genetic Programming Framework
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866914136983601152 |
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| author | Meli, Clyde Nezval, Vitezslav Oplatkova, Zuzana Kominkova Buttigieg, Victor Staines, Anthony Spiteri |
| author_facet | Meli, Clyde Nezval, Vitezslav Oplatkova, Zuzana Kominkova Buttigieg, Victor Staines, Anthony Spiteri |
| contents | Different bitstring representations can yield varying computational performance. This work compares three bitstring implementations in C++: std::bitset, boost::dynamic_bitset, and a custom direct implementation. Their performance is benchmarked in the context of concatenation within a Linear Genetic Programming system. Benchmarks were conducted on three platforms (macOS, Linux, and Windows MSYS2) to assess platform specific performance variations. The results show that the custom direct implementation delivers the fastest performance on Linux and Windows, while std::bitset performs best on macOS. Although consistently slower, boost::dynamic_bitset remains a viable and flexible option. These findings highlight the influence of compiler optimisations and system architecture on performance, providing practical guidance for selecting the optimal method based on platform and application requirements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_02897 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Performance Evaluation of Bitstring Representations in a Linear Genetic Programming Framework Meli, Clyde Nezval, Vitezslav Oplatkova, Zuzana Kominkova Buttigieg, Victor Staines, Anthony Spiteri Neural and Evolutionary Computing Artificial Intelligence Performance Different bitstring representations can yield varying computational performance. This work compares three bitstring implementations in C++: std::bitset, boost::dynamic_bitset, and a custom direct implementation. Their performance is benchmarked in the context of concatenation within a Linear Genetic Programming system. Benchmarks were conducted on three platforms (macOS, Linux, and Windows MSYS2) to assess platform specific performance variations. The results show that the custom direct implementation delivers the fastest performance on Linux and Windows, while std::bitset performs best on macOS. Although consistently slower, boost::dynamic_bitset remains a viable and flexible option. These findings highlight the influence of compiler optimisations and system architecture on performance, providing practical guidance for selecting the optimal method based on platform and application requirements. |
| title | Performance Evaluation of Bitstring Representations in a Linear Genetic Programming Framework |
| topic | Neural and Evolutionary Computing Artificial Intelligence Performance |
| url | https://arxiv.org/abs/2511.02897 |